Data Analysis in Vegetation Ecology -- Contents -- Preface -- List of Figures -- List of Tables -- 1 Introduction -- 2 Patterns in Vegetation Ecology -- 2.1 Pattern recognition -- 2.2 Interpretation of patterns -- 2.3 Sampling for pattern recognition -- 2.3.1 Getting a sample -- 2.3.2 Organizing the data -- 3 Transformation -- 3.1 Data types -- 3.2 Scalar transformation and the species enigma -- 3.3 Vector transformation -- 3.4 Example: Transformation of plant cover data -- 4 Multivariate Comparison -- 4.1 Resemblance in multivariate space -- 4.2 Geometric approach -- 4.3 Contingency testing -- 4.4 Product moments -- 4.5 The resemblance matrix -- 4.6 Assessing the quality of classifications -- 5 Ordination -- 5.1 Why ordination? -- 5.2 Principal component analysis (PCA) -- 5.3 Principal coordinates analysis (PCOA) -- 5.4 Correspondence analysis (CA) -- 5.5 The horseshoe or arch effect -- 5.5.1 Origin and remedies -- 5.5.2 Comparing DCA, FSPA and NMDS -- 5.6 Ranking by orthogonal components -- 5.6.1 Method -- 5.6.2 A numerical example -- 5.6.3 A sampling design based on RANK (example) -- 6 Classification -- 6.1 Group structures -- 6.2 Linkage clustering -- 6.3 Minimum-variance clustering -- 6.4 Average-linkage clustering: UPGMA, WPGMA, UPGMC and WPGMC -- 6.5 Forming groups -- 6.6 Structured synoptic tables -- 6.6.1 The aim of ordering tables -- 6.6.2 Steps involved -- 6.6.3 Example: Ordering Ellenberg's data -- 7 Joining Ecological Patterns -- 7.1 Pattern and ecological response -- 7.2 Analysis of variance -- 7.2.1 Variance testing -- 7.2.2 Variance ranking -- 7.2.3 How to weight cover abundance (example) -- 7.3 Correlating resemblance matrices -- 7.3.1 The Mantel test -- 7.3.2 Correlograms: Moran's I -- 7.3.3 Spatial dependence: Schlaenggli data revisited -- 7.4 Contingency tables -- 7.5 Constrained ordination -- 8 Static Explanatory Modelling.

8.1 Predictive or explanatory? -- 8.2 The Bayes probability model -- 8.2.1 The discrete model -- 8.2.2 The continuous model -- 8.3 Predicting wetland vegetation (example) -- 9 Assessing Vegetation Change in Time -- 9.1 Coping with time -- 9.2 Rate of change and trend -- 9.3 Markov models -- 9.4 Space-for-time substitution -- 9.4.1 Principle and method -- 9.4.2 The Swiss National Park succession (example) -- 9.5 Dynamics in pollen diagrams (example) -- 10 Dynamic Modelling -- 10.1 Simulating time processes -- 10.2 Including space processes -- 10.3 Processes in the Swiss National Park (SNP) -- 10.3.1 The temporal model -- 10.3.2 The spatial model -- 10.3.3 Simulation results -- 11 Large Data Sets: Wetland Patterns -- 11.1 Large data sets differ -- 11.2 Phytosociology revisited -- 11.3 Suppressing outliers -- 11.4 Replacing species with new attributes -- 11.5 Large synoptic tables? -- 12 Swiss Forests: A Case Study -- 12.1 Aim of the study -- 12.2 Structure of the data set -- 12.3 Methods -- 12.4 Selected questions -- 12.4.1 Is the similarity pattern discrete or continuous? -- 12.4.2 Is there a scale effect from plot size? -- 12.4.3 Does the vegetation pattern reflect the environmental conditions? -- 12.4.4 Is tree species distribution man-made? -- 12.4.5 Is the tree species pattern expected to change? -- 12.5 Conclusions -- Appendix A On Using Software -- A.1 Spreadsheets -- A.2 Databases -- A.3 Software for multivariate analysis -- Appendix B Data Sets Used -- References -- Index.